A new adaptive cascaded stochastic resonance method for impact features extraction in gear fault diagnosis |
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| Affiliation: | 1. State Key Laboratory for Manufacturing and Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China;2. College of Civil Engineering and Mechanics, Central South University of Forestry and Technology, Changsha 410018, China;3. The 39 Research Institute with China Electronics Technology Group Corporation, Xi’an 710065, China;1. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, 710049 Xi’an, China;2. School of Instrument Science and Engineering, Southeast University, 210096 Nanjing, China;1. Shaanxi Key Laboratory of Mechanical Product Quality Assurance and Diagnostics, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, Shaanxi Province, China;2. State Key Laboratory of Manufacturing System Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710054, Shaanxi Province, China;1. College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui 230601, PR China;2. National Engineering Laboratory of Energy-Saving Motor & Control Technology, Anhui University, Hefei, Anhui 230601, PR China;3. Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, PR China |
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| Abstract: | Gearboxes are widely used in engineering machinery, but tough operation environments often make them subject to failure. And the emergence of periodic impact components is generally associated with gear failure in vibration analysis. However, effective extraction of weak impact features submerged in strong noise has remained a major challenge. Therefore, the paper presents a new adaptive cascaded stochastic resonance (SR) method for impact features extraction in gear fault diagnosis. Through the multi-filtered procession of cascaded SR, the weak impact features can be further enhanced to be more evident in the time domain. By analyzing the characteristics of non-dimensional index for impact signal detection, new measurement indexes are constructed, and can further promote the extraction capability of SR for impact features by combining the data segmentation algorithm via sliding window. Simulation and application have confirmed the effectiveness and superiority of the proposed method in gear fault diagnosis. |
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| Keywords: | Cascaded stochastic resonance Adaptive Impact signal detection Gear fault diagnosis |
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